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Network Forensics using Deep Q-networks and Explainable Artificial Intelligence 8

Network Forensics using Deep Q-Networks and Explainable Arti...

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8th ieee International conference on Computing, Communication, Control and Automation, ICCUBEA 2024

作者： Solanki, Meghana Chaudhari, Sangita D. Y. Patil Deemed to be University RAIT Computer Engineering Department Nerul India

ISBN: (纸本)9798350391770

The increasing complexity and frequency of Distributed Denial-of-Service (DDoS) attacks demand sophisticated and effective network forensics methodologies. Utilizing Deep Q networks (DQN) and Explainable Artificial Intelligence (XAI) techniques such as local Interpretable Model-Agnostic Explanations (LIME) and DeepSHAP (SHapley Additive exPlanations), our approach initiates with the collection of extensive data from various network endpoints. Employing k-Means Clustering for data preprocessing enhances dataset quality, a critical factor for accurate pattern detection and reducing redundancy. Transforming data into multiple representations extracts essential features, exposing hidden events that signal DDoS attacks. Our core approach involves network forensics analysis using a sophisticated DQN Classifier capable of identifying and analyzing intricate DDoS attack patterns, significantly reducing false positives and adapting to new attack vectors. Integration of LIME and DeepSHAP for XAI enhances interpretability, facilitating comprehension of the model's decisions and fostering trust. By employing this methodology, organizations can substantially improve the accuracy and effectiveness of their network forensics efforts, maintain compliance with relevant regulations, and adapt to the ever-changing DDoS attack patterns that characterize the dynamic cybersecurity landscape. © 2024 ieee.

关键词： K-means clustering

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DeepVNE: Deep Reinforcement and Graph Convolution Fusion for Virtual Network Embedding 16

DeepVNE: Deep Reinforcement and Graph Convolution Fusion for...

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16th International conference on COMmunication Systems and networks, COMSNETS 2024

作者： Keerthan Kumar, T.G. Kb, Aneesh Siddheshwar, Anagha Marali, Amulya Kamath, Anupama Koolagudi, S.G. Addya, Sourav Kanti National Institute of Technology Department of Computer Science and Engineering Karnataka India Siddaganga Institute of Technology Department of Computer Science and Engineering India Pes University Department of Computer Science and Engineering India

ISBN: (纸本)9798350383119

Network virtualization (NV) plays a crucial role in modern network management. One of the fundamental challenges in NV is allocating physical network (PN) resources to the demands of the virtual network requests (VNRs). This process is known as a virtual network embedding (VNE) and is NP-hard. Most of the existing approaches for VNE are based on heuristic, meta-heuristic, and exact strategies with limited flexibility and the risk of being stuck in local optimal solutions. In this concern, we provide a deep reinforcement learning (DRL) and graph convolution network (GCN) fusion for VNE (DeepVNE) for maximizing the revenue-to-cost ratio. The DeepVNE takes advantage of the power of actor-critic models within the DRL framework to detect network states and provide optimal solutions matched to current conditions. DeepVNE effectively captures the structural dependencies of both VNRs and PN resources by GCNs, allowing better decision-making during the embedding. Considering several features in the agents throughout the training phase improves resource utilization. The experiments show that DeepVNE outperforms the baselines by gaining a 51% acceptance ratio and 28% revenue-to-cost ratio. © 2024 ieee.

关键词： Graph Convolutional Network Network Virtualization Reinforcement Learning Virtual Network Embedding

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Text Feature Representation via LSTM and Manifold Learning 4

Text Feature Representation via LSTM and Manifold Learning

引用

4th International conference on Neural networks, Information and Communication Engineering, NNICE 2024

作者： Song, Qiujun Huang, Yanyan Huang, Changbin Nanjing University of Science and Technology School of Automation Nanjin China Jiangsu University School of Computer Science and Communication Engineering Zhenjiang China

ISBN: (纸本)9798350394375

As we all know, LSTM is a very effective text feature representation model based on deep learning, so it has excellent performance in text classification and sequence labeling problems. In this paper, we propose a novel text feature representation model based on LSTM, which creatively incorporates manifold learning methods, which can maintain the geometric structure between sentences. First, we obtain the vector representation of text based on LSTM, which contains text sequence information. Then, manifold learning method is used to construct the adjacency graph structure of text representations to characterize the local geometric structure information of text representations, thereby revealing the essential laws between texts. By introducing manifold learning methods, our model can learn the potential relationships between texts, which can help improve the performance of downstream tasks. In common natural language tasks such as text classification and named entity recognition, we designed some experiments to compare the proposed model with the conventional LSTM models. The experimental results show that the model incorporating manifold learning achieves better performance than the baseline model. © 2024 ieee.

关键词： Long short-term memory

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Theoretical Analysis of an Adaptive Closeness Centrality-Based Algorithm for Dynamic Optimization of Transportation networks

Theoretical Analysis of an Adaptive Closeness Centrality-Bas...

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2024 International conference on Engineering Management of Communication and Technology, EMCTECH 2024

作者： Mann, Michael Afeka - the Academic College of Engineering School of Software Engineering and Computer Science Tel Aviv Israel

ISBN: (数字)9798331507909

ISBN: (纸本)9798331507909

Purpose: This paper presents a theoretical analysis of the DynaTrans algorithm, a novel approach for dynamic optimization of urban transportation networks. Design/methodology/approach: We introduce an Adaptive Closeness Centrality (ACC) metric and the DynaTrans algorithm, providing formal proofs of correctness, convergence, and efficiency. The analysis employs graph theory, algorithmic complexity theory, and competitive analysis techniques. Findings: We prove that DynaTrans converges to a local optimal state in O(|V|/ϵ) iterations, with each iteration has limited computational and memory requirements. The algorithm achieves solutions within a factor of O(log|V|) from the global optimum, outperforming simple greedy approaches. Practical implications: DynaTrans offers a theoretically sound foundation for real-time traffic management systems, potentially improving urban mobility and reducing congestion. Originality/value: This work introduces a new paradigm for dynamic transportation network optimization, combining adaptive centrality measures with efficient graph algorithms. The rigorous theoretical analysis provides a solid basis for practical implementation and future research in urban traffic management. © 2024 ieee.

关键词： Traffic congestion

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Design and Implementation of a 5G/4G/Wi-Fi Enabled Video Stream Analysis System for Unicast and Multicast Streaming

Design and Implementation of a 5G/4G/Wi-Fi Enabled Video Str...

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2024 ieee Middle East conference on Communications and Networking, MECOM 2024

作者： Mahato, Rajesh Kumar Technion - Israel Institute of Technology Faculty of Electrical and Computer Engineering Haifa Israel

ISBN: (纸本)9798350376715

The surge in video streaming has created a pressing need for efficient delivery mechanisms and robust real-time analysis capabilities. This paper presents a comprehensive video stream analysis system designed to operate seamlessly across 5G, 4G, and Wi-Fi networks, handling both unicast and multicast streaming. By leveraging the versatility of FFmpeg and MistServer, the system offers extensive real-time analysis capabilities, regardless of the transmission protocol, media container format, or video/audio coding standard. This adaptability makes the system well-suited for the diverse and dynamic nature of internet media *** paper outlines the system's architecture, details the implementation of its FFmpeg-based decoding and analysis processes, and discusses the integration of 5G, 4G, and Wi-Fi networks to enhance performance. Through rigorous evaluations, the system's scalability, reliability, and ability to maintain high-quality streaming across different network environments are demonstrated. These findings highlight the system's potential to significantly improve multimedia delivery and provide a foundational framework for future advancements in video stream analysis. © 2024 ieee.

关键词： Wireless local area networks (WLAN)

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Heterogeneous Wireless Network Evaluation using VIKOR Approach 1

Heterogeneous Wireless Network Evaluation using VIKOR Approa...

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1st International conference on Cognitive Computing and Engineering Education, ICCCEE 2023

作者： Kumar, Rakesh Varshney, Neeraj GLA University Department of Computer Engineering and Applicationss Mathura India

ISBN: (纸本)9798350332803

Wireless networks offer flexible and diverse wireless connectivity by integrating cellular networks, wireless LANs, and temporary networks with the Internet. A diversified network connects computers and other devices to different operating systems and/or protocols. For instance, a complex network would connect local Microsoft Windows and Linux-based personal computers to Apple Macintosh computers. As wireless communication networks continue to evolve, they are becoming more complex and integrated, combining different wireless and wired networks into what is known as multivariate networks, where each network has its unique protocols and characteristics. While identical sensor networks have te same nodes, multifaceted sensor networks have multiple types of nodes. Device-To-device and multifaceted networks are essential to the success of 5G networks. Many networks have fixed location-based stations or small clls, each with a different range and type of connection with the mobile network operator. All computers have an identical or similar network. In this study, we evaluated five networks, named Network 1, 2, 3, 4, and 5 (Mbps) allowed Bandwidth (Mbps), and Utilization (%). After the evaluation, we found that the VIKOR method is the most ideal solution for short-distance and negative-best solutions, and the solution with the longest distance from the solution determines, but the comparison of these distances does not consider the importance. As a result, Network 1 ranked first, whereas Network 3 ranked the lowest. © 2023 ieee.

关键词： Heterogeneous networks

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Graph Neural Network Based on Spectral Parameter Estimation for Mobile networks 10

Graph Neural Network Based on Spectral Parameter Estimation ...

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10th International conference on Engineering and Emerging Technologies, ICEET 2024

作者： Jin, Renhong Yan, Xin School of Computer Science and Artificial Intelligence Wuhan University of Technology Wuhan430070 China

ISBN: (纸本)9798331532895

For online learning, Graph Neural networks(GNNs) often require real graph data for parameter fine-tuning, but obtaining the graph data is challenging. Currently, GNNs are trained based on existing graph data. When learning in dynamic networks, spatial-domain GNNs use the adjacency matrix of entire graphs as its input, which are extremely resource-intensive in terms of computation and storage while dealing with large-scale graph data. Moreover, spectral-domain GNNs suffer from high spectral decomposition complexity. To enhance the generalization of GNN models and achieve lightweight GNN models, this paper proposes a lightweight GNN model based on spectral domain, SDGAT(Spectral-Domain Graph Attention Network). By combining spectral domain graph convolution and attention mechanism, SDGAT can effectively capture the global structure and local features of a graph, which helps to suppress noise in the graph and improve the robustness of the model. Furthermore, a distributed estimation algorithm for dynamic network spectral parameters is proposed, which significantly reduces the computational overhead when processing large-scale graph data. Finally, authors validate the effectiveness and accuracy of SDGAT through classification and regression tasks, and explore the feasibility of extending this model to other GNNs. © 2024 ieee.

关键词： Graph neural networks

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Infrared Drone Target Recognition Based on Global-local Context Aggregation with Self-Attention 5

Infrared Drone Target Recognition Based on Global-Local Cont...

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5th International conference on Machine Learning and computer Application, ICMLCA 2024

作者： Zhao, Liang Zhang, Yan College of Electronic Science and Technology National University of Defense Technology National Key Laboratory of Science and Technology on ATR Changsha410073 China

ISBN: (纸本)9798331530334

As drone usage increases, the security risks linked to unauthorized flights have become more evident. Infrared imaging detection technology, recognized for its robust anti-interference features and ability to provide continuous surveillance, effectively monitors drone activities in urban areas. To tackle the issues arising from the limited infrared characteristics of drones and their close resemblance to background elements-which leads to low real-time detection and recognition rates-we created a detailed urban infrared drone target dataset and introduced the Infrared Unmanned Aerial Vehicle You Only Look Once(IUAV-YOLO) network model. This model is based on global-local context aggregation with self-attention. Our method starts with a Backbone Feature Extraction Framework (BFEM) aimed at maintaining semantic information for small targets within deep networks, while a multi-branch convolution broadens the receptive field to improve local perception. We also present a self-attention context aware module Spatial Pyramid Pooling Fast Global Average Max(SPPF-GAM) to combine multi-scale contextual features, enhancing spatial awareness. Finally, the Spatial Context Aware Module(SCAM) and CSP Bottleneck With Two Convolutions(C2f) modules are linked to merge global and local context across both channel and spatial dimensions, effectively addressing the subtle features of small drones against intricate backgrounds. Experimental findings show that the IUAV-YOLO model improves the Mean Average Precision (mAP@O.S) by 4.3% compared to the baseline model, while also decreasing the number of parameters by 433,636. Overall, our proposed network demonstrates significant benefits over other leading real-time detection algorithms, making it an asset for enhancing security in urban settings. © 2024 ieee.

关键词： Target drones

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TGIEN: An Interpretable Image Editing Method for IoT Applications Based on Text Guidance 17

TGIEN: An Interpretable Image Editing Method for IoT Applica...

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ieee Congress on Cybermatics: 17th ieee International conference on Internet of Things, iThings 2024, 20th ieee International conference on Green Computing and Communications, GreenCom 2024, 17th ieee International conference on Cyber, Physical and Social Computing, CPSCom 2024, 10th ieee International conference on Smart Data, SmartData 2024

作者： Wang, Yifei Pan, Mengzhu Li, Qianmu Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9798350351637

In response to the demands and challenges of image editing in the Internet of Things (IoT) domain, we have proposed an interpretable image editing method guided by text(TGIEN), aiming to address the limitations of existing image editing models based on Generative Adversarial networks (GANs) for better application in IoT. This method utilizes a Contrastive Language-Image Pre-trained Model (CLIP) to guide the editing of the StyleGAN latent space, enabling manipulation of real images through text prompts. The model incorporates a mapping module conditioned on text embeddings to edit the latent space code of StyleGAN and learns an attention map corresponding to the text across all layers of StyleGAN. These attention maps highlight focus areas during editing, providing an intuitive explanation of the model's behavior. Additionally, during the image generation process, the attention map functions as a mask to guide local fusion between original and modified features. Furthermore, the model integrates editability losses to ensure that editing operations align closely with the text prompts. Through the aforementioned design, spatial disentanglement is explicitly achieved, enhancing the transparency and interpretability of the model and in turn increasing the capacity of image editing technology in the IoT domain for practical application and system optimization. Both qualitative and quantitative experiments demonstrate the superiority of this method over other state-of-the-art methods in terms of editing quality and decoupling of edits. In the IoT domain, this method holds promise for applications such as smart surveillance systems, smart healthcare and virtual reality, offering more intelligent and efficient image processing and analysis for these scenarios. © 2024 ieee.

关键词： Generative adversarial networks

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Bi-Directional Long Short Term Memory with Dynamic Time Wrapping for Time Series Classifications 3

Bi-Directional Long Short Term Memory with Dynamic Time Wrap...

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3rd International conference on Disruptive Technologies, ICDT 2025

作者： Raghuvanshi, Akash Katiyar, Ramakant Chandra, Nilesh School of Computer Science and Engineering Galgotias University Greater Noida India Axis Institute of Technology and management Kanpur India Kamla Nehru Institute of technology Sultanpur India

ISBN: (纸本)9798331519582

Time series data includes both global and regional patterns. Most currently used nature networks concentrate on local features rather than their connections. The latter is likewise crucial but more problematic to investigate due to the difficulty in employing feature networks to gather enough representations. This research focus to design a new model with the help of 'Dynamic Time Warping (DTW)' and Bidirectional Long Short-Term Memory (Bi-LSTM). This model encompasses processes such as gathering data, extracting features, and performing classification. Here, DTW is used for the feature extraction to get the significant features. Then, a DTW -based extracted feature is fed to the classification stage, the classification results are obtained through the utilization of 'Bidirectional Long Short-Term Memory' Further, the experimental results have verified the superior performance of the designed proposed model over other existing time series classification approaches. © 2025 ieee.

关键词： 5 Manhattan Distance Bidirectional Long Short-Term Memory Dynamic Time Warping Feature Extraction Time series Classification

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